Recent outputs – on Digital China; Digital platforms; Digital agriculture; Digital health – from Centre for Digital Development researchers, University of Manchester:
DIGITAL CHINA
China’s Digital Expansion in the Global South: Systematic Literature Review and Future Research Agenda (open access) by Richard Heeks, Angelica V. Ospina, Christopher Foster, Ping Gao, Xia Han, Nicholas Jepson, Seth Schindler & Qingna Zhou. This paper reviews the literature about China’s growing digital presence in the low- and middle-income countries of the global south. It outlines seven key issues relating to this growth, and describes a six-part future research agenda.
Special Issue of The Information Society on “China’s Digital Expansion in the Global South”, edited by CDD members. Six papers discuss China’s digital impact in Latin America, North Africa and Asia covering issues including platforms, e-commerce, technology transfer and digital surveillance.
Fair Work in South Africa’s Gig Economy: A Journey of Engaged Scholarship (open access) by Jean-Paul Van Belle, Kelle Howson, Mark Graham, Richard Heeks, Louise Bezuidenhout, Pitso Tsibolane, Darcy du Toit, Sandra Fredman & Paul Mungai. The paper describes the specific conditions which supported the take-off of location-based digital labour platforms in South Africa, and reflects on lessons learned from the Fairwork action research project in the country.
Identity Platforms and Anti-LGBTQ+ Legislation: Implications for Safeguarding Personal Data by Katherine Wyers, Brian Nicholson, Scott Russpatrick & Silvia Masiero. Focusing on countries where governments have introduced anti-LGBTQ+ legislation, this paper identifies the potential for enrolment of identity platform technology from intended into realities of use in identifying LGBTQ+ individuals and groups, identifying risks of data-induced harm and routes to overcoming them.
To some, the arrival and penetration of digital platforms in the vast informal economies of the global South cities may sound no longer new. To others, it still is a momentous opportunity to transform the informal sector. As the bustling streets fuse into the virtual marketplaces, the dynamics of transactions, trust, and community are being reshaped. To explore the renewed landscape of informal economies in the platform age, we illustrate the changing nature of trust by drawing on our recent research on platform use in the informal sector in Jakarta, Indonesia.
Broadly, there are two important impacts of platformisation in the informal sector: incorporation and legitimisation. Platforms are seen to incorporate the informal sector into the broader economy. In doing so, certain platform features aim to guarantee the credibility and transparency of informal practices that are often deemed untrustworthy without active regulations in place. While some might frame this process as “formalisation of the informal sector”, in fact, platformisation itself hardly formalises existing sectors. What it does is simply insert the previously informal or marginalised groups into the wider urban economy, both as consumers and as traders.
Then the legitimising effect is the impact of this insertion on trust building. What enables this trust are the new platform features (e.g., ratings/reviews and digital payment) that replace the traditional ways of building reputation and making transactions in the informal economy. Linking platforms with trust somewhat disguises us into thinking that there was simply no trust in the informal sector before platforms. On the contrary, trust was the most important factor that sustained the informal sector – word of mouth shaped trust and trust worked as an important “informal” institution even in the absence of formal regulations1. In other words, with nothing much but mutual trust that people could resort to, trust has been the basis for their informal trade.
With these incorporation and legitimisation effects, platformisation leads to evolving dynamics of trust amongst informal workers and consumers. In the traditional informal sector, the boundary of the interaction and social networks was largely within the physical informal city. The key change in the platformised city is that everyday interactions move beyond this physical informal space towards wider virtual networks that involve not only the informal class but also the middle and elite classes. That is, now the boundary that forms word-of-mouth has become much wider. The ratings and reviews formed by broader consumers (or simply the general public) shape reputations of platformised informal firms, not just the word-of-mouth formed within their existing social networks confined to the physical city.
These dynamics are illustrated in our research in Jakarta. Here are several remarks and comments from the motorcycle drivers, street vendors and consumers in the informal sector, who now have become part of ride-hailing, delivery, and fintech platforms. These provide a glimpse of how differentiated their experiences are, and intriguingly, the way they express distrust against one another. For example:
When asked about any negative or exploitative experiences working as platform drivers, they often mention how consumers or restaurant owners are treating them.
“Sometimes passengers/customers use threatening or aggressive language”
“Their policies prioritise consumers. Drivers should only comply with the rules and ethical codes, which is not always easy”
“Platforms’ customer service only makes consumers more talkative”
“Platform fees include parking fees, but sometimes restaurants charge us these additional fees (due to misunderstandings of rules)”
On the other hand, being entrepreneurs (albeit micro in scale), street vendors exercise more control over their business.
“I run this business, so I should take care of my customers myself. I can handle them myself and they’re (platforms) not part of it”
“In light of customer satisfaction, platform intervention is only a form of passive monitoring. Each business is already proactive in seeking solutions on its own”
In the meantime, when asked about their overall experiences with platform use, some consumers are not entirely happy with drivers.
“Platform providers should carry out regular evaluations of workers on their digital platforms. So that workers with bad and dangerous ratings do not continue to work on the platform or are trained so that they do not endanger consumers”
“Platforms should try to increase motorcycle drivers’ digital literacy”
Overall, the reality of platformisation is less aligned to claims of economic opportunities in incorporation and legitimacy in formalisation. Rather it presents new, more challenging domains around trust. With strong consumer beliefs in platforms’ contribution to transparency and a sense of distrust towards worker behaviour, these seem to amplify the distrust citizens already had against the informal sector (and vice versa). Adding fuel to this distrust may be heightened competition even amongst the drivers, street vendors and entrepreneurs.
This might eventually hamper city-wide collective movements. With platform ecosystems still dependent on existing socio-cultural moral norms and class, negotiations between agents of differential power arise and contribute to shifting the consumer culture in platform use2. Although there is growing conscientisation of the often exploitative and adverse impacts of platforms on workers across the globe, with a lack of trust, widespread collective action around platforms is less evident in the Indonesian context (but still, drawing only from a handful of comments above, we would not say it is conclusive yet).
In the end, this is neither to romanticise traditional informality nor to critique consumers. Of course, trust only cannot fully fill the voids of formal institutions that are to serve broader purposes such as safety, protection as well as market efficiency. The solution also does not seem to be in denouncing “platform algorithms” that can make contributions to filling some (but with equal importance, not all) of the voids. Nevertheless, paying attention to the evolving dynamics of trust will shed new light on the way we understand the impacts of platforms and trust in the informal economy.
As this evidence shows, we need to examine further the intersections of platforms and informality. What are the dynamics of legitimisation and extraction, control and autonomy, and order and freedom to make our platformised cities healthier?
References
1 Burbidge, D (2013) ‘Trust creation in the informal economy: the case of plastic bag sellers of Mwanza, Tanzania’, African Sociological Review, 17(1): 79-103.
Odera, L.C (2013) ‘The role of trust as an informal institution in the informal sector in Africa’, Africa Development, 38(3-4): 121-146.
PropTech (‘property technology’) refers to the use of digital technologies in the delivery of products and services in the real estate industry. Its primary goal is to innovate and optimize the ways in which properties are built, bought, sold, and managed. For example, artificial intelligence has been used to generate marketing materials and calculate credit scores; blockchain technologies have been employed to limit the risk of fraud; and virtual reality technologies have been applied to facilitate remote viewing and purchase.
PropTech is indeed a very broad sector, encompassing AI design tools, listing and management platforms, and smart home technologies, among other things, and it overlaps with other sectors such as FinTech (‘financial technology’) and ConTech (‘construction technology’). According to Zion Market Research, the global PropTech market size stood at US$19.5 billion in 2022 and is predicted to grow to US$32.2 billion by 2030.
In contrast to the hype in the business circle, academics studying the housing market have expressed concerns regarding the wide applications of PropTech. Desiree Fields (2022), a leading scholar on this topic, argues that PropTech has transformed housing financialization in the post-2008 era, particularly in the context of the single-family rental market where large-scale investment companies are able to exploit the US foreclosure crisis and forge a new asset class. With respect to the rising popularity of online listing platforms, Geoff Boeing, Julia Harten, and Rocio Sanchez-Moyano (2023) report that the already-advantaged communities tend to benefit more from these platforms. Moreover, Wainwright (2023) highlights that, while rental platforms claim to be objective, prejudices are usually built into their algorithmic designs. All these findings speak to the fact that PropTech has reconfigured the relationships among real estate professionals, investors, property owners, and tenants, as Joe Shaw (2020) argues.
To mitigate the negative impacts of PropTech on housing, Geoff Boeing et al. (2023) suggest that policymakers and practitioners: 1) make use of the data collected by online platforms to better understand market conditions; 2) collaborate with platform owners to improve algorithmic designs; 3) regulate platforms in terms of their data collection, processing, and usage. While these suggestions sound promising, their implementation seems challenging. Given the private nature of online platforms, they might be hesitant to share data, collaborate with the public sector, or fully comply with guidelines and regulations. Take Airbnb as an example, the platform has been accused of allowing its hosts to evade taxes and regulations and exaggerating housing crises in various localities.
Currently, there is a dearth of research on the social science aspects of PropTech, particularly its implications for equity, inclusion, and regulatory challenges. This gap could potentially be addressed by researchers specializing in development studies, science and technology studies, and urban studies. Compelling questions include, but are not limited to: How does the adoption of PropTech impact housing accessibility and affordability, particularly for disadvantaged groups? How does PropTech influence the dynamics of property ownership, rental markets, and housing finance? How does PropTech shape the global landscape of real estate in terms of the flow of investment, policies, and technologies? Please reach out if you are interested to discuss these questions further.
To respond to this question, I will use three main characteristics to highlight the differences and similarities between the traditional taxi industry and the ride-hailing platform industry in Lagos, particularly regarding drivers’ experiences.[1]
Vehicle Access
Access to taxis such as the Yellow taxis between the 1990s to 2007, was mainly done in-person. Throughout the city, yellow taxis had designated car parks where each driver had to write down their name on a roster and wait in their vehicles for passengers. For drivers, picking up trips was on a turn-by-turn basis. A passenger walks into the park, identifies a destination of choice, and the driver will state the cost of the journey, usually a little higher than the going rate. For example, a passenger requesting to be taken to Victoria Island, a prominent Central Business District in Lagos, from Victoria Garden City, which is about 20km away. The driver could quote a fee of N10,000 (£18), and the passenger would refute this amount and haggle the price down to an affordable rate of about N4,000 (£7). In certain instances, drivers can get away with this, especially for passengers who are not familiar with the route. In speaking to traditional taxi drivers, they highlight this as a critical strategy for surpassing their daily and weekly targets. However, when a driver refuses to accept a reduced price by a passenger, the job could fall to the next willing driver in the queue.
For ride-hailing platforms such as Uber, which emerged in Nigeria in 2014, accessing the vehicle was done simply via the platform app.[2] The platform app acts as an intermediary between passengers and drivers, removing the need to hail a vehicle, haggle for prices, and track trips. The only resource required is the smartphone, which algorithmically matches passengers’ demand and drivers’ supply. For example, if passenger A requests a trip via the app, the algorithm will sort for the nearest available driver, usually within 2km, and the driver is expected to accept. Sometimes, the driver may cancel the request due to traffic, low fares, or other unforeseen circumstances.
With ride-hailing platforms like Uber, the cost of the trip was often cheaper than traditional taxis, which quickly made it more accessible and popular amongst users.
Networks of Solidarity
Traditional taxi parks were coordinated by unions such as the Road Transport Employers Association (RTEAN), Lagos State Taxi Drivers and Cab Operators (LSTDCOA) and so forth.[3] Drivers are meant to belong to at least one union to access taxi parks as well as pay monthly dues to union executives. Without being part of a faction, drivers often have to ply the road to be accessed by passengers. This would usually mean drivers are operating illegally, according to an interview with the former transport policymaker Mustapha in December 2018. The dues collected from drivers often do not trickle down to supporting the affairs of drivers in terms of the provision of social security and safety nets.[4] However, the drivers who are part of unions and designated taxi parks undergo monthly meetings with the scope of supporting each other, lobbying for improved recognition from the State and for social protection provisions for taxi drivers.
Ride-hailing platforms entirely dismantle the need for unions and networks of socialisation or solidarity. Being classified as independent contractors or driver-partners, the business model embodies working isolation, such that drivers are typically not attached to traditional unions or taxi parks. Drivers are not required to pay monthly dues because their contributions are institutionalised via the app as commissions per trip. For example, Uber takes 25% of commission after every trip. However, the isolating nature of ride-hailing platform work has led to virtual communities such as social media and communication networks (e.g., Facebook and WhatsApp groups), facilitating collective learning about their work and resistance strategies against algorithmic control. It has further boosted the formation of collective worker groups, associations, and platform unions, and is reducing work isolation and helping the demand for decent working conditions for all drivers.
Payment and Ownership Models
The traditional taxi industry in Lagos operated mainly on three ownership models. One is the hire-purchase model, where an individual purchases a vehicle, does the necessary paperwork and employs a driver who pays to own the vehicle ultimately. The second is the lease or rental model, where drivers pay weekly sums to rent the vehicle, which was not common in the traditional taxi industry. Thirdly, outright ownership, i.e., drivers who bought their vehicles without any financial arrangements with third parties.
In the traditional taxi industry, all three ownership models were common and to some extent facilitated by the Lagos State Government. Taxis were acquired from the Lagos State government and corporate entities. For example, between 2009 to 2015, the former governor of Lagos State, Babajide Tunde Fashola, facilitated jobs for over 1000 drivers with fleets of taxis using funds from the Union Bank of Nigeria and the Lagos-Microfinance Scheme.[5]Drivers are given these vehicles and expected to pay weekly sums to own it after three to four years. Based on the economy between the 1990s to early 2000s, there is no accurate account of how much drivers were meant to remit to vehicle owners. However, based on interviews, there was an agreed weekly remittance to vehicle owners. There were no social media platforms to advertise such opportunities. It was based on trust and, in rare instances, advertisements in newspapers. Trust was only built on a track record of meeting weekly targets with little or no complaints. However, it was not transparent for vehicle owners to see how much drivers made. Vehicle owners manually managed taxis, with guarantors serving as a medium of transparency and accountability, but not enough to facilitate trust. One interviewee (2019), Taiwo (61), an elderly taxi driver, highlighted that taxi drivers made over N30,000 (£52) after costs with only a few daily trips. Taxi drivers did not have to work incessantly to make ends meet. Vehicle owners had to believe the narratives of what drivers were making weekly to negotiate weekly payments. Considering most traditional taxi drivers were under taxi unions, it was easier to collectively agree not to disclose the accurate sum of drivers’ weekly earnings.
While ride-hailing platforms embodied similar ownership models, the labour process was more quantifiable due to data and algorithmic functionalities. For instance, the dashboard on ride-hailing platform interfaces shows detailed breakdown of drivers hourly, weekly and monthly activities which boosts transparency for vehicle owners which can lead to overworking, i.e., working longer hours than expected compared to traditional taxi drivers. From interviews in this study, platform drivers noted the need for their colleagues not to post dashboards showing weekly earnings, because this gives vehicle owners and rental companies more negotiating power to demand higher weekly remunerations. It also facilitated a complicated subcontracting arrangement between drivers and the actual vehicle owner which can be classified as a third-party or proxy ownership. The vehicle owner puts an experienced driver or a non-driver in charge of a fleet of vehicles, with drivers having to make payments via the proxy.[6] For example, speaking with Ewoma, a legal practitioner, highlighted how the payment structure and works. Ewoma. managed at least 13 Uber/Bolt drivers, where they had to pay N35,000 (£61) weekly to her, and she pays N30,000 (£52) to the vehicle owner and keeps N5000 as the management fee. Within the drivers she managed, she highlighted one driver who also managed two other drivers, which facilitated more precarious pathways in making payments towards vehicle usage or ownership.
Conclusion
I will conclude by noting that while ride-hailing platforms appear to be rather different from the traditional taxi industry of Lagos, they have created new problems and not wholly solved old challenges in the taxi industry. While there are more factors elsewhere that show the differences between platforms and traditional taxis, this blog has only intricately discussed three as summarised in Table 1.
Table 1: The Difference between Traditional Taxis and Ride-hailing Platform Gig Work
Factors
Gig Work in Lagos
Taxis
Platforms
Vehicle Access
Passenger access is by hailing a cab or receiving calls
A push of a button on a smartphone; trips are assigned based on ratings and as a function of demand and supply
Fare calculation is based on the driver’s discretion or haggling, and payment is often in cash
Calculated automatically by algorithms and paid in cash or via the app
Networks of Solidarity
Union dues monthly payment and meetings
Not mandatory for platform drivers.
Union or association membership to boost collective bargaining.
Working in isolation due to independent contracts.
Union dues monthly payment and meetings
Not compulsory for drivers.
Assigned to motor parks
No assignment of parks
Managing the labour process outside a taxi park is based on the driver’s discretion and knowledge of the city
Algorithmically managed with integrated maps and GPS tracking
Payment and Ownership Models
Ownership models especially hire-purchase and leasing models were straightforward for drivers.
Payment and ownership models became transparent but more complicated for drivers.
For instance, the hope that technology and information will improve the insecurity and lack of safety has not happened. Platform drivers are now exposed to higher levels of kidnapping, robbery, assault, and death.[7] This is inherent in the fact that taxi driving, whether online or offline, is risky. Platform drivers still choose to work offline to game the system and improve their earnings, thereby exposing them to similar risks as traditional taxi drivers. Drivers are working more now than 10 to 15 years ago because of more information and avenues for vehicle owners and platform companies to track working behaviours. This does not indicate that platforms earn significantly higher than their counterparts, considering that they often offer cheap fares as a competitive strategy. It indicates that ride-hailing platforms are a different kind of traditional taxis only because of the advantage of technology. While some differences are external to the socio-technical system of platforms as highlighted, the experiences of drivers are often similar. Therefore, the question for the future will be, how can digital labour platforms be significantly better than traditional taxis?
[3] Albert, I. O. (2007). NURTW and the Politics of Motor Parks in Lagos and Ibadan. In L. Fourchard (Ed.), Gouverner les villes d’Afrique. Etat, gouvernement local et acteurs privés. Paris: Karthala.
[4] Agbiboa, D.E. (2017). Mobile Bodies of Meaning: City Life and the Horizons of Possibility. Journal of Modern African Studies, 55(3), pp.371–393.
[6] Interview with Ewoma in July 2019. Ewoma still did this side-gig until 2022, because it was profitable for her. Many of the vehicle owners, were too busy to manage the affairs of drivers.
“Bouncing back” in tourism should not be about connecting local providers to platforms but ensuring that available online tools provide inclusive outcomes
As tourism has become global it has become an important part of the economy in a number of countries of the global south. It brings foreign currency into the economy and provides a surprising number of jobs to those who provide services. For all the ethical and environmental issues it poses, in countries such as Thailand, Indonesia, Tanzania and Rwanda, the loss of tourists during the pandemic led to crises. Vast swathes of workers and firms have had to move into other sectors with broader implications for economies.
A major imperative following the crisis has been to ensure that tourism can bounce back. Institutions such as the OECD and the UN, as well as development donors and governments have pushed recovery plans with significant “digital tourism” components. Embracing digital tourism is seen as a quick win. Tourists have grown more used to online platforms – whether that be booking hotels, arranging transportation, sharing tourism experiences or posting reviews. The vision of recovery plans is that if local providers can embrace platforms, not only will they become more efficient, but drive forward tourism demand.
Drawing on recent research examining platform practices of small/marginal tourism service providers in Indonesia and Rwanda [1], we argue these visions for digital tourism may have limits. This research highlights three major considerations: the contexts of the adoption of platforms by tourism firms, the inflexibility of tourism platforms, and how tourism development may better be guided by grassroots online practices.
Platform use by small service providers
With several decades of investment in internet connectivity, the costs and barriers to internet use have been reducing, leading to growing use. This is especially the case for businesses in tourism, where digital tourism is becoming the norm. In both Indonesia and Rwanda, major global platforms such as TripAdvisor, Uber, Booking.com, Airbnb, Google maps and Traveloka are now well-established.
With the growing ubiquity, we might say that platforms are moving from something that forward-thinking firms opt into, to being non-negotiable for all firms. It is now almost like an infrastructure that firms need to be part of. This is true even amongst more marginal service providers such as tour guides, tiny hotels and those providing cultural activities who would use mobile devices to be part of such platforms.
Whether they want to go online or not, they are aware they are being mapped, rated and discussed online.
The complexity of tourism platforms
At first glance platforms seem to offer significant potential. They are easy to sign up for tourism providers and provide a way to quickly reach and interact with tourists across the globe. They often offer services such as online payments and booking systems that can make operations more efficient.
However, for small tourism providers platforms remain a challenge. While it is easy to join, successfully harnessing these platforms requires a broad range of technical skills. Successful firms need to be adept at website design, digital media skills and social media use to be able to stand out.
Challenges are not just about the capabilities of service providers, platforms are often highly complex and inflexible. For example, in Rwanda, small hotels were spending time and resources trying to move up search rankings on platforms. In Indonesia, some providers of tourism services were trying to negotiate algorithmic pricing systems.
With local support from platforms often non-existent and limited flexibility, small providers in these countries often suffered in competition with larger and foreign providers who were better places to make gains from being online.
Agency of tourism service providers
Even with these significant challenges, small service providers were able to combine digital tools for benefits – using shared calendar software, mobile apps, cloud sharing, online translation and social media to collaborate with customers and better fit with their daily needs.
Moreover, in Indonesia some tourism enterprises have come together to collaborate in more social- or environmentally-orientated online spaces. In some other countries, we have also seen the success of commercial platforms more attuned to small enterprise needs and activities (e.g. South African platform Nightbridge)
These types of activity are very different to the policy prescriptions of joining the platform “juggernauts” for pandemic recovery. They suggest alternative ways forwards for small tourism providers – by amplifying the bottom-up activities already occurring outside mainstream platforms, and by being aware that service providers are negotiating multiple platforms and online software.
Summary: Bouncing back and “digitalisation”
These experiences of tourism and the goals of pandemic recovery are mirrored in other sectors in the global south. Governments and donors are not sitting back but seeking to play an active part in recovery through support. And, like tourism, one of the areas that are repeatedly mentioned is “digitalisation” – supporting so-called “inefficient” small firms to connect and use digital platforms for economic gain.
But as this research shows, the reality is that platforms pose challenges. Connecting online is often no longer the major barrier. Rather platforms fit poorly with the skills of small firms and their growing complexity favour better-financed firms. They rarely adapt to the challenges faced in global south contexts.
Blindly shepherding firms towards adopting large platforms may negatively affect small providers. Interventions should rather support more creative uses of technology and leverage the unique relationships and applications that could afford more inclusive outcomes.
[1] This article is based on the recently published paper:
Recent outputs – on China Digital; Data-for-Development; Digital Economy / Platforms; Digital Inclusion; Digital Water; Rights; and Sustainability – from Centre for Digital Development researchers, University of Manchester:
CHINA DIGITAL
“China’s digital expansion in the global South” presents recordings of nine presentations at a CDD international workshop that discusses the implications for the global South of China’s emergence as a digital superpower.
“Digital public goods platforms for development: the challenge of scaling” (open access) by Brian Nicholson, Petter Nielsen, Sundeep Sahay & Johan Saebo. We articulate the notion of digital global public goods and examine the development of DHIS2, a global health platform inspired by public goods, focusing on the paradoxes that arise in the scaling process. A presentation of the paper to the Pankhurst Institute, University of Manchester is available on YouTube.
“RaFoLa: A Rationale-Annotated Corpus for Detecting Indicators of Forced Labour” (open access) by Erick Mendez Guzman, Viktor Schlegel & Riza Batista-Navarro, describes a dataset of news articles categorised according to forced labour indicators. The articles were annotated with rationales, i.e. human explanations for placing them under specific categories, to support the development of explainable AI systems.
“Hustling day in Silicon Savannah: datafication and digital rights in East Africa” (open access) by Gianluca Iazzolino, Michael Kimani & Maddo, is a cartoon on the winners and losers in Kenya’s booming tech scene. It translates, for a non-academic audience, the authors’ research on how digital technologies are reshaping the informal economy in the global South.
Does the transition to gig work improve incomes in Pakistan?
Many workers join gig work platforms in the belief that their incomes will improve, but is this borne out in practice? To investigate, the Centre for Labour Research interviewed 94 workers based on six platforms across three sectors: ride-hailing, food delivery, and personal care.
Of these, 51 were able to tell us what their previous monthly income had been in their most-recent employment prior to joining the platform[1]. Stated income varied from the equivalent of US$60 per month up to U$1,200 per month, and averaged US$220 per month[2].
After moving into gig work, average gross income was slightly higher, at US$240 per month but, as the graph below shows, there was a much more differentiated picture behind the average, with around 40% of respondents earning less gross income (red-bordered blue columns) than they had done previously.
However, as the graph also shows, things looked worse when comparing net income (orange columns). For the great majority of prior jobs, work-related costs were small (only work-to-home transport, which we calculated based on typical commuting journeys in Pakistan to be just under US$18 per month; i.e. less than 10% of average gross income). But for gig work – much of which relies on journeys by vehicle and continuous internet connectivity – the costs of petrol, maintenance and data eat heavily into gross income. In addition, for some (only a few in our Pakistan sample) there are costs of renting their vehicle.[3]
These costs represented, on average, 65% of gross income and knocked average net income for gig workers down to just US$85 per month. When we compare before-and-after for net income, then, we found more than 70% of our sample were earning less than in their previous job, and 45% earned over US$100 per month less.
This was especially an issue for ride-hailing drivers and it does reflect the particular circumstances during our interview period of late 2021 to early 2022: a drop-off in demand for travel due to Covid, and a steep rise in petrol prices. Indeed, so bad was the problem that just over a fifth – 21 of the 94 – were reporting negative income. That is, they were effectively paying to go to work as their costs exceeded their gross income; something to which the platforms responded in May 2022 by dropping the commission taken from drivers to 0%.
While recognising the challenging period for gig workers covered by our fieldwork, nonetheless, this does suggest that – by and large – gig work is not delivering the income boost that workers often hope for. They may, for example, be lured by gross income figures, not realising how much lower net income will be. Gig work does provide a livelihood – 40% of our sample were unemployed in the immediate period prior to joining – but it is not really fulfilling its promise. It also falls far from decent work standards: five-sixths of those we interviewed took home less than a living wage.
Acknowledgement: Fairwork is financed by the Federal Ministry for Economic Cooperation and Development (BMZ) commissioned by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ).
[1] Those who stated what their prior employment had been gave the following job descriptions: BPO operator, Teacher (2), Housekeeper, Shopkeeper, Gas company worker (2), Safety officer, Business person, Tanker driver, Ride-hailing driver with another platform (3), Traditional taxi driver (3), Farmer, Builder, Computer operator, Cook, Technician, Shop assistant, Domestic worker, Government worker
Recently there has been an explosion of research into digital platforms. To provide an indication of the size of the output, a quick search on Google Scholar provided 3270000 “hits”, 39900 in 2022 alone to date with publications across diverse disciplines including management, information systems, economics and more. In the realm of ICT4D, discourse has focused on how platforms may enable socio-economic development (Nicholson et al 2021) however there is a paucity of examples of empirical research on how this may be realised.
Digital platforms are defined according to their principal purpose and identifies two broad categories: transaction platforms and innovation platforms. Transaction platforms refer to a two or multi sided marketplace mediated by the platform. Innovation platforms act as “foundations upon which other firms can build complementary products, services or technologies” (Gawer, 2009, p. 54).
Most prior empirical research on digital platforms involves commercial, for-profit platforms situated in the regulative institutions of the Global North. Inherent in this prior work is an assumption of “monetisation” and the capitalist market forces, and little is known about platforms that are donor supported and aimed at socio economic development.
A forthcoming paper attempts to address the knowledge gap by conceptualising innovation platforms as public goods and asking:
How can innovation platforms be public goods?
A goal of the article is to identify the challenges of simultaneously scaling up digital platforms and developing them into public goods. Empirically, the focus is on health, specifically the empirical example is the District Health Information System (DHIS2).
The relevance of public goods in development is well-established in the domain of health. Initiatives driven by global health organisations such as the World Bank and World Health Organization aim to promote digital public goods. Digital Square, a marketplace initiative in digital health, has developed a Global Goods Guidebook and a Global Goods Maturity Model. Before and during the pandemic, open-source systems have been launched to support outbreak management, such as the Surveillance Outbreak Response Management and Analysis System (SORMAS). SORMAS intuitively displays features of a public good: it is free of charge, open source, independent from tech companies, and interoperable with other platforms such as DHIS2.
Turning to theory of public goods leads us to the economics discipline and centres on two main principles: non-rivalry and non-exclusion. “Goods” such as crime control, flood defences etc. are provided because of failure of the market mechanism. Government thus intervenes either financially, through such mechanisms as taxation or licensing, or with direct provision. Public goods are non-rivalrous, implying that one individual’s consumption of the good does not influence what is available for others. They are also non-excludable, in the sense that no one can be excluded from consumption of a public good.
Consider a lighthouse where one navigator’s use of the light does not prevent other navigators from doing the same. Many potential public goods exhibit only one of these properties resulting in the tragedy of the commons which can be illustrated with the example of a village pasture. Unrestricted access (non-exclusion) to the commons – pasture belonging to the village as a whole – leads to its degradation (rivalry). However, some scholars question the inevitability of depletion of common pool resources when they are managed in a bottom-up, cooperative way by those most dependant on them. Under certain conditions, individuals govern themselves collectively, and without market pressures or government regulation, to obtain benefits, even if the temptation to freeride is present.
Global public goods are goods whose benefits cross borders and are global in scope for example eradication of infectious diseases where it is impossible to exclude any country from benefiting and each country will benefit without preventing another.
The district health information system or DHIS2 supports decentralized routine health management. The architecture is designed with a generic core that enables local innovation and anyone with internet access can at any time download the most recent version of DHIS2, the source code, as well as required libraries and third-party products (such as Chrome or Firefox browsers). DHIS2 also comes with a set of bundled apps, developed by University of Oslo or through its partners in the Global South (such as HISP Tanzania, an independent entity with close collaboration with Oslo) available in an “app store.” It is similar in concept to Apple App Store or Google Play and some DHIS2 apps are also available on these platforms too. The platform architecture allows local innovation as apps, increasing its potential relevance globally.
Due to its openness and flexibility, it is impossible to know the exact number of DHIS2 implementations. It is known that ministries of health and other organizations in more than 100 developing countries use DHIS2, together covering an estimated population of 2.4 billion people. In November 2020, the ministries of health in 73 countries (primarily developing countries) used DHIS2, out of which 60 were nationwide implementations, and 13 were in the pilot stage. In addition, 22 Indian states used DHIS2. There is also a range of other organizations using DHIS2 independently for reporting in the countries they are operating, including PEPFAR, Médecins Sans Frontières (MSF), International Medical Corps, Population Services International (PSI), and Save the Children.
We can explore the “qualification” of DHIS2 as a public good by considering some of the challenges experienced by developers in Oslo and other implementation sites examined as tensions and paradoxes. In a seminal paper on paradoxes and theory building, Poole and van de Ven (1989) identify a paradox as “concerned with tensions and oppositions between well-founded, well-reasoned, and well-supported alternative explanations of the same phenomenon” (565).
Consider the story of the product lead of the DHIS2 analytics team response to the challenge of prioritizing requests by developing a roadmap prioritization matrix. Most use-cases need analytics functionality, and a wide variety of requests are directed to this team. The product lead estimates that the analytics team can only accommodate about half the requests at any stage of the product development cycle. The question facing this individual is: “which requests should be prioritized, coming from whom, and in which release cycle?” The primary implementations of DHIS2 are users from governments in low- and middle-income countries, according to the product lead, who tend to not actively voice their requests for changes in functionality. These groups are constrained by physical separation often across great distance, limiting ability to meet in person and develop social relationships. By contrast, users from donor organizations and other users in the West, tend to have closer proximity and resources to visit Oslo and “make their voices heard,” resulting in greater influence over the DHIS2 functionality development. This mismatch led the product manager to develop this “objective” prioritization methodology. From the perspective of public goods, the dynamics of donors’ activity affects the rivalry / excludability conditions as their greater influence means that other users are relatively excluded, and access is rivalrous depending on this influence.
There are also paradoxical consequences of scaling at the macro and micro levels. While the Oslo development team add in their releases of new features for strengthening outputs and analysis towards a generic global platform, the typical user in a district of a developing country requires basic functionalities, and the new features often detract instead of increasing the software’s value for the users. At the macro-level, the development team are seeking to cater to the universe of users, including district users, researchers, and data analytic experts in multiple country contexts. This requires them to continuously add new features, often for increasingly sophisticated use. This process went counter to the needs at the micro-level of the users in district offices, who want specific and easy to use functionalities for their everyday use. Thinking again from a theoretical standpoint, the malleability of a digital good compared to the oft cited example of a static lighthouse is clearly evident. The drive towards generic global features at the macro level causes rivalry and excludes some users at the local more micro level.
Overall, the more macro interests of the donors and drive towards a global generic platform appear incompatible with the smaller players who become increasingly marginalized. Furthermore, their capacity for collective action is limited by structural factors. This challenges DHIS2’s status as a public good as we can see rivalry and exclusion creeping in.
The problem is not insurmountable, collective action and subsidiarity offer helpful mechanisms of governance. Two main subsidiarity conditions are known to be helpful related to effectiveness and necessity: that action should be taken at the level where it is most effective and that action at the higher level should be taken when lower levels cannot achieve the set goals by themselves. This is in line with ongoing efforts by Oslo to build South-South community-based networks and thereby decentralization into the Health Information System Programme (HISP) network.
References
Gawer, A. (2009). Platform dynamics and strategies: from products to services. Platforms, markets and innovation, 45, 57.
Nicholson, B., Nielsen, P., & Sæbø, J. (2021). Digital platforms for development. Inf. Syst. J., 31(6), 863-868.
Recent outputs – on Data-for-Development; Digital Labour; Digital Platforms; Digital Society; Ed Tech; MSc Programme – from Centre for Digital Development researchers, University of Manchester:
“Stripping Back the Mask: Working Conditions on Digital Labour Platforms during the COVID-19 Pandemic” (open access) by Kelle Howson, Funda Ustek-Spilda, Alessio Bertolini, Richard Heeks and other colleagues from the Fairwork project, analyses the Covid policies of 191 platforms in 43 countries. It finds some positive worker protections but also entrenchment of precarious work as platforms leverage the opportunities arising from the crisis.
DIGITAL PLATFORMS
“Digital Platforms for Development” (open access) by Brian Nicholson, Petter Nielsen & Johan Saebo, provides an editorial introduction to a special issue of Information Systems Journal on the link between digital platforms and development processes.
“Driving the Digital Value Network: Economic Geographies of Global Platform Capitalism” (open access) by Kelle Howson, Fabian Ferrari, Funda Ustek-Spilda, Richard Heeks and other colleagues from the Fairwork project, uses insights from global value chain and global production network frameworks to analyse power imbalances and value extraction across territories by gig economy platforms.
DIGITAL SOCIETY
“Toolkit for Measuring Digital Skills and Digital Literacy“ (open access) by authors at CSIS Indonesia, supported by Matthew Sharp, offers a comprehensive and original framework for measuring digital skills in Indonesia and other G20 countries. The toolkit incorporates insights from pilot individual and firm-level surveys on digital skills undertaken by CSIS in the Greater Jakarta area.
“How can Smart City Shape a Happier Life? The Mechanism for Developing a Happiness Driven Smart City” by Huiying Zhu, Liyin Shen & Yitian Ren, introduces a Happiness Driven Smart City (HDSC) mechanism, composed of a three-layer structure and underpinned by a set of strategic measures. A case study shows the HDSC mechanism’s effectiveness in helping decision makers understand the status quo, strengths and weaknesses of smart city development in their context, so that their SC blueprint can be better aligned towards a happiness-driven direction.
Centre for Digital Development staff provide the core directorship and teaching for the University’s new MSc programme in Digital Development, which will launch in Sept 2022.
When a consumer pays for motorcycle-based gig work, where does the money go?
Following the approach of an earlier, similar post on car ride-hailing, and again using data gathered by the Fairwork Indonesia team in Jakarta, we can break this down using the generic model shown below:
a. Amount paid by customer: the service payment plus a platform fee (sometimes called an order or service or transaction processing fee) plus – sometimes – a tip.
b. Amount paid to platform: platforms typically take a commission (a set percentage of the customer service payment, usually between 10-25%) and often also charge a platform fee.
c. Amount paid to worker: all of the tip and the service payment minus the platform’s commission. In some instances – at the end of a shift or at the end of a week – the worker might also get a bonus payment from the platform e.g. for completing a certain number of tasks or being available for work consistently and/or at particular times. There may also be other criteria that impact access to bonus payments such as low order cancellation rates or high customer feedback ratings. Bonuses are paid to the worker from the platform’s share which is taken from the platform’s commission; sometimes also from the platform fee; and in some instances more than this (in other words, in these cases, the worker earns more than the amount paid by the customer due to an additional subsidy taken by the platform from investment or other sources of capital).
The two charts below show the distribution of customer payments for two motorcycle-based gig work platforms (which were charging a 20% gross commission on the customer service payment plus a fee). Figure 1 presents data for riders who own their own motorcycle (the majority of riders in our sample). Figure 2 presents data for riders who finance their vehicle through loan repayments or (less frequently) rental.
We can draw a number of conclusions:
i. Shares of the Pie:the worker’s true net income (i.e. after work-related costs have been taken into account) is a significant share – around two-thirds – of the total payment made by the customer. Aside from the net income earned by the worker, the great majority of the customer payment is captured by large private businesses; typically multinationals – the platform, fuel companies, vehicle finance houses, telecom providers. A significant chunk of vehicle servicing and maintenance costs even goes this way via parts, oil, tyres, etc.
ii. Fuel Costs: fuel makes up a very significant proportion of costs: around 80% of costs for bike owners; about half of costs for those who finance their motorcycle. It is therefore not surprising that the price of fuel is always at the forefront of workers’ minds: a relatively small rise can cause quite a significant reduction in their net income.
iii. Financing vs. Owning: as expected, the net income of those who finance their vehicle is a lower proportion of customer payment than that of vehicle owners. In absolute terms, these two groups take home about the same net income (non-owners’ net income was about 5% lower). It’s not completely clear how this happens but one contributing factor is that workers who finance their bikes work longer hours in order to help towards earning the extra to cover their repayments: an average 78-hour week compared to a 66-hour week for those who owned their bikes.
iv. Bonuses and Platform Subsidies: as noted below, the figures here are calculated on the basis of 23.5% of rider income deriving from platform bonus payments. The platform gross commission plus fee represent just over 32% of the customer payment; yet the platform’s net earning is 5% or 6% only. In other words, and absent unknown factors, the platform is on average paying substantially more than its entire commission to workers.
On this basis, one can calculate the tipping point at which platforms earn nothing and are having to subsidise worker income from investment or other sources of capital. As illustrated in Figure 3, for this instance, this will happen when worker bonuses make up more than 30% of their income. Yet one can find examples in Indonesia where the effect of bonuses is to more than double workers’ basic pay (i.e. bonuses make up more than 50% of worker income). In such circumstances platforms must be significantly subsidising gig work from capital. If this is widespread, it may help to explain why so many gig work platforms report operating losses.
Network effects – the greater value of a platform to users as more users participate – would predict the emergence of monopoly (single seller of services to customers) and monopsony (single buyer of services from workers). Yet this has not happened in most gig economy markets – including those of Indonesia – which, instead, are oligopolies/oligopsonies, meaning there is competition between platforms for both customers and workers. It is that competition which in part motivates the payment of bonuses to workers.
Notes:
– Although insurance is shown as 0%, there are small payments against this item by some workers; just that they are so negligible a component that they rounded down to zero percent.
– The average figures we have included are that 25% of rider income is made up from tips and bonuses, of which tips make up 1.5%. This must be seen as a very rough-and-ready average because platforms’ bonus payment schemes are continuously changing; their availability typically varies between workers (e.g. with tiered systems such that the highest bonus payments are only accessible by workers who meet particular criteria on workload, availability, cancellation rates, customer ratings, etc.); and workers’ ability to meet the targets necessary for bonus payment varies from day to day. Bonuses are typically also only achievable for those working very long shifts: some of our sample were working 15- and in a couple of instances 18-hour days.
– The figures here do not take into account any customer-side promotions that platforms occasionally run; the assumption being that these may not alter the share of rider income.
– Fairwork data from South Africa showed riders’ net income to be 55% of the total customer payment, but this did not separately account for bonuses, which will increase the percentage. Overall, distribution of income will vary between platforms and locations so the figures above should be seen as illustrative rather than universal.
– Research work reported in this blogpost was supported by the German Federal Ministry for Economic Cooperation and Development (BMZ), under a commission by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ).
Post by Richard Heeks, Treviliana Putri, Paska Darmawan, Amri Asmara, Nabiyla Risfa, Amelinda Kusumaningtyas & Ruth Simanjuntak.
Recent outputs – on Digital China; Digital platforms; Digital agriculture; Digital health – from Centre for Digital Development researchers, University of Manchester:
Recent outputs – on China Digital; Data-for-Development; Digital Economy / Platforms; Digital Inclusion; Digital Water; Rights; and Sustainability – from 
Recent outputs – on Data-for-Development; Digital Labour; Digital Platforms; Digital Society; Ed Tech; MSc Programme – from 
ICTs for Development 